Data-driven molecular modeling with the generalized Langevin equation

•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduct...

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Vydané v:Journal of computational physics Ročník 418; s. 109633
Hlavní autori: Grogan, Francesca, Lei, Huan, Li, Xiantao, Baker, Nathan A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cambridge Elsevier Inc 01.10.2020
Elsevier Science Ltd
Predmet:
Approximation
Coarse-grained models
Complexity
Computational physics
Data-driven parametrization
Dimension reduction
Generalized Langevin equation
Granulation
Mathematical analysis
Molecular dynamics
Molecular dynamics
Coarse-grained models
Dimension reduction
Generalized Langevin equation
Data-driven parametrization
ISSN:0021-9991, 1090-2716
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Popis
Shrnutí:•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation.•Approximate GLE predicts non-equilibrium properties, like autocorrelation, well. The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.
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ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2020.109633